Examples of Macro Trading Factors

The below sections illustrate the construction of trading factors from quantamental indicators and test plausible propositions. They indicate significant predictive power and economic trading value of intelligent concepts across all asset classes. All research can principally be replicated and modified, although Jupyter notebooks have only been added for research posts from May 2023. The posts and notebooks are not trading recommendations or realistic strategy backtests, but are proofs of concepts based on the simplest implementation of an investment idea. Also, there is an academic research support program that sponsors data sets for relevant projects.

Cross Asset Class Factors

Indicators of growth and inflation cycles are plausible and successful predictors of asset class returns. For proof of concept, we propose a single balanced “cyclical strength score” based on point-in-time quantamental indicators of excess GDP growth, labor market tightening, and excess inflation. It has clear theoretical implications for all major asset markets, as rising operating rates and consumer price pressure raise real discount factors.

Empirically, the cyclical strength score has displayed significant predictive power for equity, FX, and fixed income returns, as well as relative asset class positions. The direction of relationships has been in accordance with standard economic theory. Predictive power can be explained by rational inattention. Naïve PnLs based on cyclical strength scores have each produced long-term Sharpe ratios between 0.4 and 1 with little correlation with risk benchmarks. This suggests that a single indicator of cyclical economic strength can be the basis of a diversified portfolio.

Bank lending surveys help predict the relative performance of equity and duration positions. Signals of strengthening credit demand and easing lending conditions favor a stronger economy and expanding leverage, benefiting equity positions. Signs of deteriorating credit demand and tightening credit supply bode for a weaker economy and more accommodative monetary policy, benefiting long-duration positions.

Empirical evidence for developed markets strongly supports these propositions. Since 2000, bank survey scores have been a significant predictor of equity versus duration returns. They helped create uncorrelated returns in both asset classes, as well as for a relative asset class book.

Excess inflation means consumer price trends over and above the inflation target. In a credible inflation targeting regime, positive excess inflation skews the balance of risks of monetary policy towards tightening. An inflation shortfall tips the risk balance towards easing. Assuming that these shifting balances are not always fully priced by the market, excess inflation in a local currency area should negatively predict local rates market and equity market returns, and positively local-currency FX returns.

Indeed, these hypotheses are strongly supported by empirical evidence for 10 developed markets since 2000. For fixed income and FX excess inflation has not just been a directional but also a relative cross-country trading signal. The deployment of excess inflation as a trading signal across asset classes has added notable economic value.

Unsterilized central bank interventions in foreign exchange and securities markets increase base money liquidity independently from demand. Thus, they principally affect the money price of all assets. Since intervention policies are often persistent, reported trends are valid predictors of future effects. If markets are not fully macro information efficient, sustained relative intervention liquidity trends, distinguishing more supportive from less supportive central banks, are plausible predictors of the future relative performance of assets across different currency areas.

Indeed, empirical evidence suggests that past trends of estimated intervention liquidity help predict future relative return performance of equity index futures, long-long equity-duration positions, and FX positions across countries.

Risk-parity positioning in equity and (fixed income) duration has been a popular and successful investment strategy in past decades. However, part of that success is owed to a supportive macro environment, with accommodative refinancing conditions and slow, disinflationary, or even deflationary economies. Financial and economic shocks, as opposed to inflation shocks, dominated markets, leading to a negative equity-duration correlation. The macro environment is changeable, however, and a strong theoretical case can be made for managing risk-parity strategies based on economic trends and risk-adjusted carry.

We propose simple strategies based on macro-quantamental indicators of economic overheating. Overheating scores have been strongly correlated with risk parity performance and macro-based management would have even benefited risk parity performance even during the past two “golden decades” of risk parity.

Equity Factors

Macroeconomic trends affect stocks differently, depending on their lines of business and their home markets. Hence, point-in-time macro trend indicators can support two types of investment decisions: allocation across sectors within the same country and allocation across countries within the same sector. Panel analysis for 11 sectors and 12 countries over the last 25 years reveals examples for both.

Across sectors, export growth, services business sentiment, and consumer confidence have predicted the outperformance of energy stocks, services stocks, and real estate stocks, respectively. Across countries, relative export growth, manufacturing sentiment changes, and financial conditions have predicted the outperformance of local stocks versus foreign ones for the overall market and within sectors.

Market price trends often foster economic trends that eventually oppose them. Theory and empirical evidence support this phenomenon for equity markets and suggest that macro headwind (or tailwind) indicators are powerful modifiers of trend following strategies. 

As a simple example, we calculate a macro support factor for equity index futures in the eight largest developed markets based on labor markets, inflation, and equity carry. This factor is used to modify standard trend following signals. The modification increases the predictive power of the trend signal and roughly doubles the risk-adjusted return of a stylized global trend following strategy since 2000.

The dividend discount model suggests that stock prices are negatively related to expected real interest rates and positively to earnings growth. The economic position of households or consumers influences both. Consumer strength spurs demand and exerts price pressure, thus pushing up real policy rate expectations. Meanwhile, tight labor markets and high wage growth shift national income from capital to labor.

This post calculates a point-in-time score of consumer strength for 16 countries over almost three decades based on excess private consumption growth, import trends, wage growth, unemployment rates, and employment gains. This consumer strength score and most of its constituents displayed highly significant negative predictive power with regard to equity index returns. Value generation in a simple equity timing model has been material, albeit concentrated on business cycles’ early and late stages.

Employment growth is an important and underestimated macro factor of financial market trends. Since the expansion of jobs relative to the workforce is indicative of changes in slack or tightness in an economy it serves as a predictor of monetary policy and cost pressure. High employment growth is therefore a natural headwind for equity markets. Similarly, the expansion of jobs in one country relative to another is indicative of relative monetary tightening and economic performance. High relative employment growth is therefore a tailwind for the local currency. 

These propositions are strongly supported by empirical evidence. Employment growth-based trading signals would have added significant value to directional equity and FX trading strategies since 2000.

Academic research suggests that high and rising consumer price inflation puts upward pressure on real discount rates and is a headwind for equity market performance. 

A fresh analysis of 17 international markets since 2000 confirms an ongoing pervasive negative relation between published CPI dynamics and subsequent equity returns. Global equity index portfolios that have respected the inflation dynamics of major currency areas significantly outperformed equally weighted portfolios. Even the simplest metrics have served well as warning signals at the outset of large market drawdowns and as heads-ups for opportunities before recoveries. The evident predictive power of inflation for country equity indices has broad implications for the use of real-time CPI metrics in equity portfolio management.

Fixed Income Factors

The pace of aggregate demand in the macroeconomy exerts pressure on interest rates. In credible inflation targeting regimes, excess demand should be negatively related to duration returns and positively to curve-flattening returns. Indeed, point-in-time market information states of various macro demand-related indicators all have helped predict returns of directional and curve positions in interest rate swaps across developed and emerging markets.

The predictive power of an equally weighted composite demand score has been highly significant at a monthly or quarterly frequency and the economic value of related strategies has been sizeable.

Local-currency import growth is a widely underestimated and important indicator of trends in fixed-income markets. Its predictive power reflects its alignment with economic trends that matter for monetary policy: domestic demand, inflation, and effective currency dynamics.

Empirical evidence confirms that import growth has significantly predicted outright duration returns, curve position returns, and cross-currency relative duration returns over the past 22 years. A composite import score would have added considerable economic value to a duration portfolio through timing directional exposure, positioning along the curve, and cross-country allocations.

The fiscal stance of governments can be a powerful force in local fixed-income markets. On its own, an expansionary stance is seen as a headwind for long-duration or government bond positions due to increased debt issuance, greater default or inflation risk, and less need for monetary policy stimulus. Quantamental indicators of general government balances and estimated fiscal stimulus allow backtesting the impact of fiscal stance information.

Empirical evidence for 20 countries since the early 2000s shows that returns on interest rate swap receiver positions in fiscally more expansionary countries have significantly underperformed those in fiscally more conservative countries. Indicators of fiscal stance have been timely, theoretically plausible, and profitable criteria for fixed-income allocations across currency areas.

Real government bond yields are indicators of standard market risk premia and implicit subsidies. They can be estimated by subtracting an estimate of inflation expectations from standard yields. And for credible monetary policy regimes, inflation expectations can be estimated based on concurrent information on recent CPI trends and the future inflation target. 

For a data panel of developed markets since 2000, real yields have displayed strong predictive power for subsequent monthly and quarterly government bond returns. Simple real yield-based strategies have added material economic value in 2000-2023 by guiding both intertemporal and cross-country risk allocation.

Classic trend following is based on market prices or returns. Market trends are relatively cheap to produce, popular, and plausibly generate value in the presence of behavioral biases and rational herding. Macro trends track relevant states of the economy based on fundamental data. They are more expensive to produce from scratch and generate value due to rational information inattentiveness. While market trends are timelier, macro trends are more specific in information content. Due to this precision, they serve better as building blocks of trading signals without statistical optimization and are easier to predict based on real-time information.

Reason and evidence suggest that macro and market trends are complementary. Two combination methods are [1] market information enhancement of macro trends and [2] market influence adjustment of macro trends. Indicators of fiscal stance have been timely, theoretically plausible, and profitable criteria for fixed-income allocations across currency areas.

Broad macroeconomic trends, such as inflation, economic growth, and credit creation are critical factors of shifts in monetary policy. Above-target trends support monetary tightening. Below-target dynamics give grounds for monetary easing. Yet, markets may not fully anticipate policy shifts that follow macro trends, for lack of attention or conviction. In this case, macro trends should predict returns in rates markets.

In the past, even a very simple point-in-time macro pressure indicator, an average of excess inflation, economic growth, and private credit trends, has been significantly correlated with subsequent rates receiver returns, both in large and small currency areas. Looking at the gap between real rates and macro trend pressure delivers even higher forward correlation and extraordinary directional accuracy with respect to fixed income returns.

Inflation expectations wield great influence over fixed income returns. They determine the nominal yield required for a given equilibrium real interest rate, they influence inflation risk premia, and they shape the central bank’s course of action. There is no uniform inflation expectation metric than can be tracked in real-time. However, there are useful and complementary proxies, such as market-based breakeven inflation and economic data-based estimates. For trading strategies, these two can be combined.

The advantage of breakeven rates is the real-time tracking of a broad range of influences. The advantages of economic data-based estimates are clarity, transparency, and precision of measurement. Changes in both inflation metrics help predict interest rate swap returns, but their combination is a better predictor than the individual series, emphasizing the complementarity of market and economic data.

Duration volatility risk premium means compensation for bearing return volatility risk of an interest rate swap (IRS) contract. It is the scaled difference between swaption-implied and realized volatility of swap rates’ changes. Historically, these premia have been stationary around positive long-term averages, with episodes of negative values. 

Two derived concepts of volatility risk premia hold promise for trading strategies. [1] Term spreads are the differences between volatility risk premia for longer-maturity and shorter-maturity IRS contracts and are related to the credibility of a monetary policy regime.

Historically, term spreads have been significant predictors of returns on curve positions. [2] Maturity spreads are the differences between volatility risk premia of longer- and shorter-maturity options and should be indicative of a fear of risk escalation, which affects mainly fixed receivers. Indeed, maturity spreads have been positively and significantly related to subsequent fixed-rate receiver returns. 

Foreign Exchange Factors

FX forward-implied carry is a popular ingredient in currency trading strategies because it is related to risk premia and implicit policy subsidies. Its signal value can often be increased by considering inflation differentials, hedging costs, data outliers, and market restrictions. However, even then, FX carry is an imprecise and noisy signal, and previous research has shown the benefits of enhancements based on economic performance (view post here). 

This post analyses the adjustment of real carry measures by currency over- or undervaluation. As a reference point, it uses point-in-time metrics of purchasing power parity-based valuation estimates that are partly or fully adjusted for historical gaps. The adjustment is conceptually compelling and has historically increased the performance of carry signals across a variety of strategies.

Regression-based statistical learning helps build trading signals from multiple candidate constituents. The method optimizes models and hyperparameters sequentially and produces point-in-time signals for backtesting and live trading.

This post applies regression-based learning to macro trading factors for developed market FX trading, using an improved cross-validation method for expanding panel data. Sequentially optimized models consider nine theoretically valid macro trend indicators to predict FX forward returns. The learning process has delivered significant predictors of returns and consistent positive PnL generation for over 20 years. The most important macro-FX signals, in the long run, have been relative labor market trends, manufacturing business sentiment changes, relative inflation expectations, and terms of trade dynamics.

Trend following can benefit from consideration of macro trends. One reason is that macroeconomic data indicate headwinds (or tailwinds) for the continuation of market price trends. This is particularly obvious in the foreign-exchange space. For example, the positive return trend of a currency is less likely to be sustained if concurrent economic data signal a deterioration in the competitiveness of the local economy.

Macro indicators of such setback risk can slip through the net of statistical detection of return predictors because their effects compete with dominant trends and are often non-linear and concentrated. As a simple example, empirical evidence shows that standard global FX trend following would have benefited significantly merely from adjusting for changes in external balances.

Pure macro(economic) strategies are trading rules that are informed by macroeconomic indicators alone. They are rarer and require greater analytical resources than standard price-based strategies. However, they are also more suitable for pure alpha generation. This post investigates a pure macro strategy for FX forward trading across developed and emerging countries based on an “external strength score” considering economic growth, external balances, and terms-of-trade.

Rather than optimizing, we build trading signals based on the principles of “risk parity” and “double diversification.” Risk parity means that allocation is adjusted for the volatility of signals and returns. Double diversification means risk is spread over different currency areas and conceptual macro factors. Risk parity across currency signals diminishes vulnerability to idiosyncratic country risk. Risk parity across macroeconomic concepts mitigates the effects of the seasonality of macro influences. Based on these principles, the simplest pure macro FX strategy would have produced a long-term Sharpe ratio of around 0.8 before transaction costs with no correlation to equity, fixed income, and FX benchmarks.

There are two simple ways to enhance FX carry strategies with economic information. The first increases or reduces the carry signal depending on whether relevant economic indicators reinforce or contradict its direction. The output can be called “modified carry”. It is a gentle adjustment that leaves the basic characteristics of the original carry strategy intact. The second method equalizes the influence of carry and economic indicators, thus diversifying over signals with complementary strengths. The combined signal can be called “balanced carry”.

An empirical analysis of carry modification and balancing with economic performance indicators for 26 countries since 2000 suggests that both adjustments would have greatly improved the performance of vol-targeted carry strategies. Modified carry would also have improved the performance of hedged FX carry strategies.

FX forward-implied carry is a valid basis for trading strategies because it is related to divergences in monetary and financial conditions. However, nominal carry is a cheap and rough indicator: related PnLs are highly seasonal, sensitive to global equity markets, and prone to large drawdowns.

Simple alternative concepts such as real carry, interest rate differentials, and volatility-adjusted carry metrics have specific benefits but broadly fail to mitigate these shortcomings. However, the consideration of a market beta premium, adjustment for inflation expectations, and the consideration of other macro-quantamental factors make huge positive differences. Not only do these modifications greatly enhance the theoretical plausibility of value generation, but they also would have almost doubled the PnL generation over the past 20 years, removed most of its equity market dependence, and greatly reduced seasonality.

Economic growth differentials are plausible predictors of foreign exchange return trends because they are related to differences in monetary policy and return on investment. Suitable metrics for testing growth differentials as trading signals must replicate historic information states. Two types of such metrics based on higher-frequency activity data are [i] technical GDP growth trends, based on standard econometrics, and [ii] intuitive GDP growth trends, mimicking intuitive methods of market economists. Both types have predicted FX forward returns of a set of 28 currencies since 2000.

For simple growth differentials, the statistical probability of positive correlation with subsequent returns has been near 100% with a quite stable relationship across time. Excess growth trends, relative to potential growth proxies, would have been more appropriate predictors for non-directional (hedged) FX forward returns. Correlations with hedged returns have generally been lower but accuracy has been more balanced. Finally, balanced growth differentials that emphasize equally the performance of output and external balances are theoretically a sounder predictor. Indeed, these indicators post even higher and more stable correlations with subsequent directional returns than simple growth differentials.

Commodity Factors

Inventory scores are quantamental (point-in-time) indicators of the inventory states and dynamics of economies or commodity sectors. Inventory scores plausibly predict base metal futures returns due to two effects. First, they influence the convenience yield of a metal and the discount at which futures are trading relative to physical stock. Second, they predict demand changes for restocking by producers and industrial consumers.

Inventory scores are available for finished manufacturing goods and base metals themselves. An empirical analysis for 2000-2024 shows the strong predictive power of finished goods inventory scores and some modest additional predictive power of commodity-specific inventory scores.

Business sentiment is a key driver of inventory dynamics in global industry and, therefore, a powerful indicator of aggregate demand for industrial commodities. Changes in manufacturing business confidence can be aggregated by industry size across all major economies to give a powerful directional signal of global demand for metals and energy. 

An empirical analysis based on information states of sentiment changes and subsequent commodity futures returns shows a clear and highly significant predictive relation. Various versions of trading signals based on short-term survey changes all produce significant long-term alpha. The predictive relation and value generation apply to all liquid commodity futures contracts.

Commodity futures carry is the annualized return that would arise if all prices remained unchanged. It reflects storage and funding costs, supply and demand imbalances, convenience yield, and hedging pressure. Convenience and hedging can give rise to an implicit subsidy, i.e., a non-standard risk premium, and make commodity carry a valid basis for a trading signal.

An empirical analysis of carry for the front futures in 23 markets shows vast differences in size and volatility, with storage costs being a key differentiator. Also, carry is, on average, not strongly correlated across commodities, making it a more diversified signal contributor. To align carry measures more closely with expected premia, one can adjust for inflation, seasonal fluctuations, return volatility, and carry volatility. Most adjusted carry metrics display highly significant predictive power for returns.

Carry on commodity futures contains information on implicit subsidies, such as convenience yields and hedging premia. Its precision as a trading signal improves when incorporating adjustments for inflation, seasonal effects, and volatility. There is strong evidence for the predictive power of various metrics of real carry with respect to subsequent future returns for a broad panel of 23 commodities from 2000 to 2023.

Furthermore, stylized naïve PnLs based on real carry point to material economic value, either independently or through managing commodity long exposure. The predictive power and value generation of relative carry signals seem to be even more potent than that of directional signals.

Unlike other derivatives markets, for commodity futures, there is a direct relation between economic activity and demand for the underlying assets. Data on industrial production and inventory build-ups indicate whether recent past demand for industrial commodities has been excessive or repressed. This helps to spot temporary price exaggerations. Moreover, changes in manufacturing sentiment should help predict turning points in demand.

Empirical evidence based on real-time U.S. data and base metal futures returns confirms these effects. Simple strategies based on a composite score of inventory dynamics, past industry growth, and industry mood swings would have consistently added value to a commodities portfolio over the past 28 years, without adding aggregate commodity exposure or correlation with the broader (equity) market.

Credit Factors

Selling protection through credit default swaps is akin to writing put options on sovereign default. Together with tenuous market liquidity, this explains the negative skew and heavy fat tails of generic CDS (short protection or long credit) returns. Since default risk depends critically on sovereign debt dynamics, point-in-time metrics of general government debt sustainability for given market conditions are plausible trading indicators for sovereign CDS markets and do justice to the non-linearity of returns.

There is strong evidence of a negative relation between increases in predicted debt ratios and concurrent returns. There is also evidence of a negative predictive relation between debt ratio changes and subsequent CDS returns. Trading these seems to produce modest but consistent alpha.